Challenge: Large language models (LLMs) are now being used by millions of people across the world.
Approach: They propose a test suite called XSTest to identify such eXaggerated Safety behaviours in a systematic way.
Outcome: The proposed test suite identifies eXaggerated Safety behaviours in a systematic way.

Similar Papers

A Chinese Dataset for Evaluating the Safeguards in Large Language Models (2024.findings-acl)

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Challenge: a recent study has shown that large language models can produce harmful responses, exposing users to unexpected risks.
Approach: They propose a dataset for the safety evaluation of Chinese LLMs in Mandarin Chinese . they extend the dataset to better identify false negative and false positive examples .
Outcome: The proposed dataset is for the safety evaluation of Chinese LLMs, and is based on a Chinese dataset.
Realistic Evaluation of Toxicity in Large Language Models (2024.findings-acl)

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Challenge: a large amount of data exposes large language models to toxicity and bias . prompt engineering can be easily bypassed with minimal prompt engineering.
Approach: They propose a dataset that uses manually crafted prompts to nullify protective layers of large language models.
Outcome: The proposed dataset shows that prompts can nullify protective layers of large language models.
Navigating the OverKill in Large Language Models (2024.acl-long)

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Challenge: Recent studies have highlighted a tendency among large language models to refuse to answer benign queries.
Approach: They propose a model-agnostic approach to reduce excessive attention to harmful words like ‘kill’ and a method to decode the next-token predictions by contrastive decoding.
Outcome: The proposed approach reduces the refusal rate by 20% while having little impact on safety.
XGUARD: A Graded Benchmark for Evaluating Safety Failures of Large Language Models on Extremist Content (2026.findings-acl)

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Challenge: Existing safety evaluations rely on binary labels, overlooking the nuanced risk these outputs pose.
Approach: They propose a framework to assess the severity of extremist content generated by Large Language Models (LLMs) it categorizes model responses into five danger levels (0–4) defined by degree of extremism endorsement .
Outcome: The proposed framework categorizes model responses into five danger levels (0–4) defined by degree of extremist endorsement, enabling nuanced analysis of failure frequency and severity.
Evaluating Psychological Safety of Large Language Models (2024.emnlp-main)

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Challenge: a recent study evaluated the psychological safety of large language models.
Approach: They designed unbiased prompts to evaluate the psychological safety of large language models.
Outcome: The proposed prompts showed that they were fine-tuned with behavioral metrics to reduce toxicity.
Can LLMs Hear the Dogwhistle? (2026.findings-acl)

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Challenge: Existing safety benchmarks focus on explicitly harmful content, but ignore context-dependent expressions such as dogwhistles.
Approach: They propose a benchmark for evaluating LLM safety under dogwhistle-driven prompts . their findings expose a blind spot in current safety evaluation practices .
Outcome: The proposed benchmark compared safety performance with toxic terms using dogwhistle-driven prompts.
Safety of Large Language Models Beyond English: A Systematic Literature Review of Risks, Biases, and Safeguards (2026.eacl-long)

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Challenge: Large language models (LLMs) have a growing number of applications that generate harmful, biased, or unsafe content.
Approach: They synthesize findings from recent studies that evaluate their robustness across languages . they highlight gaps in multilingual safety research and recommend future work .
Outcome: The systematic review examines the multilingual safety of large language models in English . it identifies challenges such as dataset availability and evaluation biases .
Mitigating Societal Harms in Large Language Models (2023.emnlp-tutorial)

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Challenge: Recent studies have highlighted societal harms that can be caused by language generation models deployed in the wild.
Approach: They propose to use a typology of technical approaches to mitigating harms of language generation models to provide an overview of potential social issues in language generation including toxicity, social biases, misinformation, factual inconsistency, and privacy violations.
Outcome: The proposed typology addresses toxicity, biases, misinformation, factual inconsistency, and privacy violations in language generation models.
WalledEval: A Comprehensive Safety Evaluation Toolkit for Large Language Models (2024.emnlp-demo)

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Challenge: Potential harms include training data leakage, biases in responses and decision-making, and unauthorized use for purposes such as terrorism and the generation of sexually explicit content.
Approach: WalledEval is a comprehensive AI safety testing toolkit designed to evaluate large language models.
Outcome: The framework supports both LLM and judge benchmarking and incorporates custom mutators to test safety against various text-style mutations such as future tense and paraphrasing.
Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable Survey (2023.eacl-main)

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Challenge: Recent advances in the capacity of large language models to generate human-like text have prompted a heated discourse around the risks of societal harms they introduce.
Approach: They propose a taxonomy of interventions organized around the different phases where they can be adopted to mitigate harms.
Outcome: The proposed methods are based on several prior works’ taxonomies of language model risks and provide an overview of strategies for detecting and ameliorating different kinds of risks/harms.

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